This week, the speaker for the IMG Seminar is Dr. Alina Zare on Addressing spatial uncertainty during remote sensing data analysis.
Most supervised machine learning algorithms assume that each training data point is paired with an accurate training label (for classification) or value (for regression). However, obtaining accurate training label information is often time consuming and expensive, making it infeasible for large data sets, or may simply be impossible to provide given the physics of the problem. Furthermore, human annotators may be inconsistent when labeling a data set, providing inherently imprecise label information.
In the case of problems with imprecise label information, Multiple Instance Learning (MIL) methods are required. The Multiple Instance Adaptive Cosine Estimator (MI-ACE) approach is one of the few MIL methods that can estimate a representative target concept. In this presentation, an introduction to the MI-ACE approach will be provided along with a description of several MIL-based algorithms and results on a variety of data types and applications.